NimbleEdge

📹 Watch NimbleEdge’s session on Real-time Personalization in Mobile Gaming at Game Developers Conference 2024! We are excited to share NimbleEdge CEO, Varun Khare’s session from GDC 2024. In the session, Varun talks through the massive potential of real-time personalization in mobile gaming, and the enormous role on-device ML can play in this space📱 Here are the key highlights from the talk: 🎮 Modern Mobile Gaming Landscape: The mobile gaming industry is evolving rapidly to become more data driven, with unprecedented user volumes. However, competition is increasing correspondingly 🤖 Importance of Machine Learning (ML) in Mobile Gaming: ML can be a pivotal differentiator in mobile gaming, with potential to significantly enhance user experience 🌩️ Challenges in ML on Cloud: Most ML today is on cloud, which is slow, expensive, privacy-invasive, and often relies on stale customer data 📲 On-Device ML and it’s use-cases: Enter on-device ML! By leveraging user devices for compute, real-time ML can be cost-efficient and privacy-preserving. This unlocks use-cases like real-time recommendations, offers, content moderation and more How NimbleEdge Helps: Despite its many benefits, optimizing real-time ML model execution across diverse devices is a massive challenge to take on in-house. NimbleEdge platform helps enterprises easily deploy and maintain ML models - users only have to upload the model, while the platform handles execution and orchestration!  Curious to learn more about how on-device ML helps with real-time personalization? Write to us at [email protected] #MobileGaming #MachineLearning #GDC2024 #AI  https://lnkd.in/gPZJhija 

⚡𝗟𝗲𝗮𝗿𝗻 𝗵𝗼𝘄 𝗡𝗶𝗺𝗯𝗹𝗲𝗘𝗱𝗴𝗲 𝗶𝘀 𝗲𝗻𝗮𝗯𝗹𝗶𝗻𝗴 𝗼𝗻-𝗱𝗲𝘃𝗶𝗰𝗲 𝗲𝘃𝗲𝗻𝘁 𝘀𝘁𝗿𝗲𝗮𝗺 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 𝗳𝗼𝗿 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗶𝗻𝗴 𝗿𝗲𝗮𝗹-𝘁𝗶𝗺𝗲 𝗱𝗮𝘁𝗮 𝗶𝗻𝗴𝗲𝘀𝘁𝗶𝗼𝗻 📱 In our post last week, we shared how NimbleEdge is enabling AI teams to capture event streams on-device for use in training session-aware personalization model 💡 However, event payloads may often be large in size, and require filtering and processing before transfer to cloud storage for use in training. NimbleEdge enables AI teams to execute this event stream processing directly on-device using Python scripts! Click on the link below to learn more: https://lnkd.in/gzCgqzUH

📲 𝐑𝐞𝐚𝐝 𝐨𝐮𝐫 𝐛𝐥𝐨𝐠 𝐭𝐨 𝐥𝐞𝐚𝐫𝐧 𝐡𝐨𝐰 𝐍𝐢𝐦𝐛𝐥𝐞𝐄𝐝𝐠𝐞 𝐢𝐬 𝐞𝐧𝐚𝐛𝐥𝐢𝐧𝐠 𝐞𝐟𝐟𝐨𝐫𝐭𝐥𝐞𝐬𝐬 𝐞𝐯𝐞𝐧𝐭 𝐬𝐭𝐫𝐞𝐚𝐦 𝐝𝐚𝐭𝐚 𝐜𝐨𝐥𝐥𝐞𝐜𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐬𝐞𝐬𝐬𝐢𝐨𝐧-𝐚𝐰𝐚𝐫𝐞 𝐀𝐈 ⏲️ Session-aware personalization, i.e. adapting app experiences to real-time user inputs, is being leveraged by several pioneering apps such as Netflix, Instacart, AirBnB and Alibaba to boost engagement and conversion. 💰 However, training models to enable such session-aware personalization requires building massive, accurate event stream datasets, which is time-taking and involves large cloud transfer costs With NimbleEdge's on-device event stream capture capabilities, requisite user clickstream data is captured and stored securely on their own devices, with provisions for seamless and cost-efficient transfer to cloud storage for training. Click on the link below to learn more: https://lnkd.in/gNHvwccZ

Watch this insightful conversation between Dr. Vinesh Sukumar of Qualcomm and Dan Costa as they discuss their expectations for the evolution of on-device AI 💡 In this discussion, they cover: 📱 Why we need AI capabilities on our phones 🚗 Edge AI beyond mobile (e.g., in automotive applications) ‼️ Challenges in on-device AI (e.g., model size, continuous learning) 🔏 Privacy preservation and personalization with on-device AI ⏳ Future developments (e.g., on-device AI agents) 📈 With mobile hardware capabilities improving exponentially, mobile apps can now leverage on-device AI to cost-effectively deliver scalable and privacy-preserving AI experiences that are impossible to achieve with cloud infra 📲 At NimbleEdge, we're building an on-device AI platform that enables apps to achieve this effortlessly. NimbleEdge platform provides tools to ingest raw user data, deploy, execute, and monitor on-device models, as well as state-of-the-art AI models optimized for mobile deployment To learn more, visit nimbleedge.com or contact us at [email protected] Watch the conversation here: https://lnkd.in/gpbfFqSh

Check out this recent blog by Google about on-device AI, which provides a clear overview of: ❓ What on-device AI really means 📱 How today’s smartphone hardware powers on-device AI 💰 The cost and latency benefits of on-device AI OS providers for smartphones and laptops have shipped on-device LLMs to deliver several valuable features. However, mobile apps still struggle to leverage the benefits of on-device AI due to the complexities of on-device model deployment and execution. At NimbleEdge, we’re solving this challenge with an on-device AI platform that enables edge modeling, deployment, event ingestion, model execution, and monitoring. Learn more at nimbleedge.com or reach out to us at [email protected]. 🔗 https://lnkd.in/efvEpBbJ

𝗖𝗵𝗲𝗰𝗸 𝗼𝘂𝘁 𝗡𝗶𝗺𝗯𝗹𝗲𝗘𝗱𝗴𝗲 𝗲𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴 𝘁𝗲𝗮𝗺 𝗺𝗲𝗺𝗯𝗲𝗿, Arpit Saxena'𝘀 𝗹𝗮𝘁𝗲𝘀𝘁 𝗯𝗹𝗼𝗴 𝗼𝗻 𝗵𝗮𝗿𝗱𝘄𝗮𝗿𝗲 𝗺𝗲𝗺𝗼𝗿𝘆 𝗺𝗼𝗱𝗲𝗹𝘀 🗒️ In the blog, Arpit breaks down hardware memory models and the complexities of relaxed concurrency, focusing mostly on ARM and IBM POWER architectures, while also motivating the C++ memory model 💡 Ideal for developers looking to deepen their understanding of low-level memory synchronization, this blog offers valuable insights into ensuring correctness while squeezing out performance! https://lnkd.in/gS9_TqX7

We are stoked to welcome Neeraj Poddar to the NimbleEdge team as our new VP of Engineering! Neeraj brings a remarkable background in building infrastructure products for massive scale to NimbleEdge. He has previously co-founded Aspen Mesh and led the engineering team at solo.io, where he also spearheaded Istio, one of the largest and foundational open-source projects in the cloud-native ecosystem ⚙️ Here, we share insights from a brief conversation with him, outlining the vast experience he brings to the organization and why NimbleEdge's vision resonates strongly with him 🗣️ https://lnkd.in/gDB7A7ZH

𝐌𝐢𝐬𝐭𝐫𝐚𝐥 𝐫𝐞𝐥𝐞𝐚𝐬𝐞𝐬 𝐧𝐞𝐰 𝐀𝐈 𝐦𝐨𝐝𝐞𝐥𝐬 𝐨𝐩𝐭𝐢𝐦𝐢𝐳𝐞𝐝 𝐟𝐨𝐫 𝐞𝐱𝐞𝐜𝐮𝐭𝐢𝐨𝐧 𝐨𝐧 𝐩𝐡𝐨𝐧𝐞𝐬 𝐚𝐧𝐝 𝐥𝐚𝐩𝐭𝐨𝐩𝐬! 📲 French AI startup, Mistral AI, which builds foundational AI models, just launched its first models designed to be run on edge devices - Ministral 3B and 8B! 🤖 This launch heats up the competition in the sub-10B parameter language model category, with Mistral claiming its models perform better than similarly sized models by peers (e.g. Google's Gemma 2 2B, Meta's Llama 3.2 3B) across benchmarks🏅 While ML engineers now have a lot of choices in terms of edge-compatible models to build with, setting up and maintaining edge AI pipelines remain highly challenging, not least due to device diversity and performance issues on resource-constrained mobile devices. That is where NimbleEdge steps in. NimbleEdge platform simplifies the entire on-device AI lifecycle for mobile apps' ML teams, enabling effortless experimentation, deployment, execution, control and monitoring. Interested in learning more? Visit nimbleedge.com or reach out to [email protected] https://lnkd.in/gSCsGJFP

Watch this succinct summary of the on-device AI features already on offer in current flagship smartphones by BBC News! 📲 With MediaTek announcing their new smartphone system-on-chip with dedicated NPU yesterday, it is clear that the possibilities with on-device AI will continue to expand rapidly⚡ ❓Mobile device manufacturers and operating systems are already capitalizing on this but performant on-device AI remains out of reach for mobile apps due to the complexity involved in edge AI experimentation, deployment, control and monitoring NimbleEdge is helping mobile apps tackle this problem with turnkey infrastructure for the complete on-device AI lifecycle! If this sounds interesting, visit nimbleedge.com to learn more or reach out to us at [email protected] https://lnkd.in/giw4hNYF

𝗡𝗲𝘄 𝗼𝗻-𝗱𝗲𝘃𝗶𝗰𝗲 𝗔𝗜 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀 𝗱𝗼𝗺𝗶𝗻𝗮𝗻𝘁 𝗶𝗻 𝗪𝗶𝗻𝗱𝗼𝘄𝘀 𝟭𝟭 𝟮𝟰𝗛𝟮 𝘂𝗽𝗱𝗮𝘁𝗲 ⚡ In a clear sign of the emerging importance of AI features in PCs, most coverage around the recent Windows update announcement has centred around new on-device AI features! 🤖 With this new update, Copilot+ PC users will be able to leverage Windows' on-device AI to: 📷 Enhance the quality of old low-resolution photographs 🖼️ Easily delete or generatively add elements to photographs ➡️ Access AI based features (e.g. object erasure, visual search) directly from the right-click menu on a snipped image In line with emerging on-device AI capabilities at an OS level, NimbleEdge helps mobile apps easily leverage on-device AI to enable truly real-time personalized experiences, using both traditional and generative AI. To learn more, visit nimbleedge.com or reach out to us at [email protected] https://lnkd.in/g39CPpVj